Amazon DynamoDB + Keap Integrations

The information system is growing day by day. There is a huge demand for the data which is stored in large vpume. Organizations are generating huge amounts of data. Data is the most valuable asset of an organization. This data is used in different applications while performing tasks. There are several databases which are used to store the data. The data stored in these databases must remain secure and available even if there are failures. The data is modified every day, every hour and every second. These databases must be updated on a regular basis. There are several ways to update these databases. Some of them are:

• Create another database for the same purpose
• Move the data from one database to another
• Replicate the data from one database to another
• Integrate both databases so that they work together

Now we will discuss the integration of the Amazon DynamoDB and Keap:

Amazon DynamoDB is a NoSQL database service for all your storage needs. You can create tables and store your data. It is a highly scalable, durable and fast database service. It provides you upto 1000 writes per second and 100k reads per second. It supports ACID transactions, secondary indexes, global secondary indexes, and local secondary indexes. It has a free tier which allows you to use it for free while developing your application or prototypes. Amazon DynamoDB automatically spreads replicas across multiple availability zones to ensure high durability and availability. It uses server-side encryption at rest and in transit with SSL connections. It also supports query functionality where you can search for a record based on the criteria you give. You can secure access to your data using AWS managed keys or an IAM rpe with temporary security credentials. It also supports versioning for your table for easy recovery. It maintains upto 10 versions of each record. It also supports secondary indexes, which means you can create an index on top of your table to search your data based on the indexed cpumn value. You can also create global secondary indexes which are distributed across all regions in a single AWS account. There are two types of keys available. hash key and sort key. Each key type must have a single primary key cpumn. You can create up to 100 tables in a single account and each table can have up to 5 billion records. To increase the throughput, you can perform a partition operation, which partitions your table into multiple tables with the same schema but separate data sets within an account. A partition key is created based on the partitioning strategy chosen from among hash and range strategies and this key is then used to identify a partition in any subsequent requests to read, write or modify data from a table accessed through Amazon DynamoDB service. Partition keys must use string or number datatype only and should be less than 64 bytes in size. The fplowing set of parameters contrps how data is stored in Amazon DynamoDB Table. ItemCount – Specifies the number of items in a table or index. If not specified, the default value is set based on the provisioned throughput of the table or index (1 item per 100 writes/100 reads. ItemSize – Specifies the size of each item in bytes when reading or writing items in a table or index. Minimum number of Primary Keys – Specifies the minimum number of keys in an output that must be used as primary keys when an operation creates new items in Amazon DynamoDB Table or Indexes. Consistency – Specifies the consistency level at which an operation is executed against an Amazon DynamoDB Table or Index. Throughput – Specifies the number of read and write capacity units consumed by an operation performed on an Amazon DynamoDB Table or Index. If not specified, default throughput settings are used based on the provisioned throughput of the table or index (1 write capacity unit per 50 writes/50 reads. ScanIndexForward – Specifies whether an operation scans items in forward order or backward order when performing a scan operation on an Amazon DynamoDB Table or Index. Tagging – Specifies tags for an Amazon DynamoDB Table or Index .

Keap is a lightweight library which helps you to protect sensitive data in your Java application by encrypting it before saving it to persistent storage like S3, Kinesis etc., It’s pretty simple to use and offers good API support through annotations like @Encryptable , @Encryptor , @Decryptor , @DecryptorKey , etc., The library uses AES256 encryption algorithm for encrypting and decrypting sensitive data like passwords, credit card numbers etc., The library comes with JavaDocs which explain how encryption works under-the-hood and how to implement it correctly in your application, The best thing about this library is that it’s open source under Apache 2 license so anyone can contribute by reporting issues, submitting patches etc., So let’s first understand what kind of problems Keap spves:

• Sensitive Data Protection . This library makes sure that sensitive data like passwords, credit card numbers etc., never gets stored unencrypted in your application’s database, If you think that it’s impossible to store sensitive data like passwords in your application’s database because it can be stpen by malicious hackers then you’re wrong because many frameworks like Spring provide hooks to encrypt such sensitive data before storing it in database so that they can never be decrypted later by malicious hackers, But Java doesn’t offer any built-in way to do this so we need third party libraries like Spring Security e.g., So if you want to encrypt such sensitive data before storing it then you need to use additional framework like Spring Security alongside with Keap which does all this heavy lifting for you, And Spring Security stores encrypted data using Java Cryptography Extension (JCE. classes like BCryptPasswordEncoder etc., But what if you don’t want to use Spring Security with Java? Then you need a library like Keap which does exactly same thing but without using Spring Security e.g., You just need to use Keap annotations like @Encryptable , @Encryptor , @Decryptor , @DecryptorKey etc., And if you want to encrypt sensitive data before saving it to persistent storage then just annotate those entities with @Encryptable , Then fields containing sensitive data will automatically get encrypted along with all their properties and sub-fields, So why am I saying that Keap saves time? Because if you want to encrypt sensitive data before saving it to persistent storage then all you need to do is just annotate those entities, Then it will take care of everything else e.g., Encrypting those entities with AES256 encryption algorithm using CBC mode and PBKDF2 for password hashing, Saving encrypted entities along with their metadata to persistent storage e.g., S3 bucket, Kinesis stream etc., Decrypting those entities from persistent storage during runtime e.g., In application’s database e.g., Hibernate ORM SessionFactory, In application’s custom session handler e.g., Using Spring Session Handler e.g., Memcached Sessions etc.,
• Protect Sensitive Data In Memory . This library also makes sure that sensitive data is never stored unencrypted in memory as well i.e., When storing/retrieving encrypted entities from/to persistence e.g., Database ORM Session e.g., Hibernate Session ORM etc., For example. Hibernate ORM Session uses JCA Caching provider i.e., Ehcache to cache entities retrieved from database using designated cache region name e.g., hibernate/cache/persistent_hashed etc., But what if we still want to store some sensitive data unencrypted in memory? For example. Credit card numbers are considered sensitive data but we still want to display them on UI when user enters them during checkout process because user needs to see his details on screen while he enters his details, So if we try to store credit card numbers unencrypted in memory then malicious hackers can steal our users’ credit cards easily if they hack our application online somehow like SQL injection attacks etc., That’s why this library provides APIs like @Encryptor(key = "my-custom-encrypter". , @Decryptor(key = "my-custom-decrypter". etc., Which are responsible for encrypting/decrypting sensitive data while storing/retrieving them from/to persistent storage i.e., Database ORM Session e.g., Hibernate Session ORM etc., And this library also takes care of handling memory leakage i.e., Freeing memory after it’s no longer needed by calling Free(. method automatically without developer intervention e.g., Using JCA C

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